DEV Community: Pranjol Das

Things Every Programmer Should NOT Do

Pranjol Das — Wed, 12 Jun 2024 05:39:02 +0000

Hello, fellow coders! 🌟 Today, let's dive into the world of programming pitfalls. Yes, we’ve all been there – making silly mistakes, facing embarrassing bugs, and having those "what was I thinking?" moments. Let’s laugh at our collective missteps and ensure we avoid them in the future.

1. Don’t Ignore Error Messages

The Classic: “Eh, it works on my machine.”

We've all been tempted to ignore those pesky error messages, but trust me, they’re there for a reason. Ignoring them is like ignoring a fire alarm because you’re too busy to leave the building.

Tip: Take the time to read and understand error messages. They’re your best friends in debugging.

2. Don’t Write Spaghetti Code

When Your Code Looks Like This:

Writing code that only you understand might feel like leaving a signature, but it’s more like leaving a mess. Write clean, readable code. Your future self (and your teammates) will thank you.

Tip: Follow coding standards and document your code.

3. Don’t Skip the Planning Phase

“I’ll just figure it out as I go.”

Jumping straight into coding without a plan is a recipe for disaster. You wouldn’t build a house without a blueprint, right?

Tip: Take the time to plan your project structure and flow before diving in.

4. Don’t Neglect Version Control

“I don’t need Git for this small project.”

Oh, but you do! Version control isn’t just for big projects. It saves you from losing your work and helps you keep track of changes.

Tip: Use Git, even for small projects. Commit early, commit often.

5. Don’t Underestimate Testing

“It worked in the last version, so it should work now.”

Testing isn’t optional. It’s the safety net that catches you before you fall.

Tip: Write tests for your code. Automated testing can save you a ton of headaches.

6. Don’t Forget to Take Breaks

“I’ll just finish this feature, then I’ll rest.”

Burnout is real, folks. Staring at the screen for hours without a break doesn’t make you productive; it makes you a zombie.

Tip: Take regular breaks. Your brain needs downtime to process and come up with creative solutions.

7. Don’t Be Afraid to Ask for Help

“I should know this. I don’t want to seem dumb.”

Everyone gets stuck. Don’t suffer in silence. Ask your peers, join forums, or find a mentor.

Tip: There’s no shame in asking for help. It’s a sign of strength, not weakness.

8. Don’t Stop Learning

“I’m done learning; I know everything I need.”

Technology evolves rapidly. The moment you stop learning, you start falling behind.

Tip: Keep up with new technologies, attend webinars, and read up on the latest trends.

Remember, programming is as much about avoiding pitfalls as it is about writing code. Laugh at your mistakes, learn from them, and keep coding!

Get Geeky with Python: Build a System Monitor with Flair

Pranjol Das — Wed, 12 Jun 2024 04:58:40 +0000

Introduction

Hey there, fellow coders! Today, I'm super excited to share an awesome project I recently worked on—a Python System Monitor. This isn't just any ordinary system monitor; it comes with a splash of color and a whole lot of flair! Whether you're a beginner looking to level up your Python skills or an experienced developer wanting to create a handy tool, this project has something for everyone. Plus, I've made it open-source, so feel free to check out the code on my GitHub repository.

I'm new to the development world, and every star on the repo will really motivate me to keep learning and building cool projects!

What Does It Do?

This Python System Monitor provides detailed information about your system's hardware, including:

System Information: OS, Node Name, Release, Version, Machine, and Processor.
CPU Information: Physical and Logical Cores, Frequency, and Usage.
Memory Information: Total, Available, Used Memory, and Swap.
GPU Information: Load, Free Memory, Used Memory, Total Memory, and Temperature.

Key Libraries Used

os and platform: For basic system information.
psutil: To fetch CPU and memory details.
GPUtil: To get GPU stats.
colorama: To add some colorful flair to the terminal output.
tabulate: To format the output into neat tables.

Breaking Down the Code

Let's dive into the code and see how it all works.

1. System Information

We start by fetching basic system details using the platform library. The get_system_info function gathers and formats these details into a table.

import platform
from colorama import Fore, Style

def get_system_info():
    uname = platform.uname()
    system_info = [
        [f"{Fore.YELLOW}System{Style.RESET_ALL}", uname.system],
        [f"{Fore.YELLOW}Node Name{Style.RESET_ALL}", uname.node],
        [f"{Fore.YELLOW}Release{Style.RESET_ALL}", uname.release],
        [f"{Fore.YELLOW}Version{Style.RESET_ALL}", uname.version],
        [f"{Fore.YELLOW}Machine{Style.RESET_ALL}", uname.machine],
        [f"{Fore.YELLOW}Processor{Style.RESET_ALL}", uname.processor]
    ]
    return system_info

2. CPU Information

Next, we use psutil to get detailed CPU information, including core counts and frequencies.

import psutil
from colorama import Fore, Style

def get_cpu_info():
    cpufreq = psutil.cpu_freq()
    cpu_info = [
        [f"{Fore.CYAN}Physical cores{Style.RESET_ALL}", psutil.cpu_count(logical=False)],
        [f"{Fore.CYAN}Total cores{Style.RESET_ALL}", psutil.cpu_count(logical=True)],
        [f"{Fore.CYAN}Max Frequency{Style.RESET_ALL}", f"{cpufreq.max:.2f}Mhz"],
        [f"{Fore.CYAN}Min Frequency{Style.RESET_ALL}", f"{cpufreq.min:.2f}Mhz"],
        [f"{Fore.CYAN}Current Frequency{Style.RESET_ALL}", f"{cpufreq.current:.2f}Mhz"]
    ]
    for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)):
        cpu_info.append([f"{Fore.CYAN}Core {i}{Style.RESET_ALL}", f"{percentage}%"])
    cpu_info.append([f"{Fore.CYAN}Total CPU Usage{Style.RESET_ALL}", f"{psutil.cpu_percent()}%"])
    return cpu_info

3. Memory Information

Memory details, including swap memory, are fetched and formatted.

from colorama import Fore, Style

def get_memory_info():
    svmem = psutil.virtual_memory()
    swap = psutil.swap_memory()
    memory_info = [
        [f"{Fore.GREEN}Total Memory{Style.RESET_ALL}", f"{get_size(svmem.total)}"],
        [f"{Fore.GREEN}Available Memory{Style.RESET_ALL}", f"{get_size(svmem.available)}"],
        [f"{Fore.GREEN}Used Memory{Style.RESET_ALL}", f"{get_size(svmem.used)}"],
        [f"{Fore.GREEN}Percentage{Style.RESET_ALL}", f"{svmem.percent}%"],
        [f"{Fore.GREEN}Total Swap{Style.RESET_ALL}", f"{get_size(swap.total)}"],
        [f"{Fore.GREEN}Free Swap{Style.RESET_ALL}", f"{get_size(swap.free)}"],
        [f"{Fore.GREEN}Used Swap{Style.RESET_ALL}", f"{get_size(swap.used)}"],
        [f"{Fore.GREEN}Percentage Swap{Style.RESET_ALL}", f"{swap.percent}%"]
    ]
    return memory_info

def get_size(bytes, suffix="B"):
    factor = 1024
    for unit in ["", "K", "M", "G", "T", "P"]:
        if bytes < factor:
            return f"{bytes:.2f}{unit}{suffix}"
        bytes /= factor

4. GPU Information

Using GPUtil, we gather GPU details and display them in a readable format.

import GPUtil
from colorama import Fore, Style

def get_gpu_info():
    gpus = GPUtil.getGPUs()
    gpu_info = []
    for gpu in gpus:
        gpu_info.append([
            f"{Fore.MAGENTA}GPU ID{Style.RESET_ALL}", gpu.id
        ])
        gpu_info.append([
            f"{Fore.MAGENTA}GPU Name{Style.RESET_ALL}", gpu.name
        ])
        gpu_info.append([
            f"{Fore.MAGENTA}GPU Load{Style.RESET_ALL}", f"{gpu.load * 100}%"
        ])
        gpu_info.append([
            f"{Fore.MAGENTA}GPU Free Memory{Style.RESET_ALL}", f"{gpu.memoryFree}MB"
        ])
        gpu_info.append([
            f"{Fore.MAGENTA}GPU Used Memory{Style.RESET_ALL}", f"{gpu.memoryUsed}MB"
        ])
        gpu_info.append([
            f"{Fore.MAGENTA}GPU Total Memory{Style.RESET_ALL}", f"{gpu.memoryTotal}MB"
        ])
        gpu_info.append([
            f"{Fore.MAGENTA}GPU Temperature{Style.RESET_ALL}", f"{gpu.temperature} °C"
        ])
    return gpu_info

5. Displaying the Information

Finally, we use tabulate to display all the collected information in a clean, readable format.

from tabulate import tabulate
from colorama import Fore, Style

def display_info():
    system_info = get_system_info()
    cpu_info = get_cpu_info()
    memory_info = get_memory_info()
    gpu_info = get_gpu_info()

    print(f"{Fore.YELLOW}{'System Information':^50}{Style.RESET_ALL}")
    print(tabulate(system_info, tablefmt="fancy_grid"))
    print("\n")

    print(f"{Fore.CYAN}{'CPU Information':^50}{Style.RESET_ALL}")
    print(tabulate(cpu_info, tablefmt="fancy_grid"))
    print("\n")

    print(f"{Fore.GREEN}{'Memory Information':^50}{Style.RESET_ALL}")
    print(tabulate(memory_info, tablefmt="fancy_grid"))
    print("\n")

    print(f"{Fore.MAGENTA}{'GPU Information':^50}{Style.RESET_ALL}")
    print(tabulate(gpu_info, tablefmt="fancy_grid"))
    print("\n")

if __name__ == "__main__":
    display_info()

Example Output

Here's what the output looks like when you run the script:

Wrapping Up

And there you have it! A stylish and functional Python System Monitor that gives you a comprehensive look at your system's performance. Feel free to clone the repository, play around with the code, and customize it to your liking. You can find the full project on my GitHub.

I'm new to the development world, and every star on the repo will really motivate me to keep learning and building cool projects. Happy coding!

Building a Web Scraping Tool with Python: Extracting News Headlines

Pranjol Das — Tue, 11 Jun 2024 07:08:39 +0000

Introduction

Web scraping allows us to automatically extract data from websites. In this tutorial, we'll use Python along with the requests and beautifulsoup4 libraries to build a web scraping tool. Our goal is to fetch news headlines from the BBC News website.

Prerequisites

Before we start, ensure you have the following:

Basic understanding of Python programming.
Python installed on your machine (Python 3.6 or higher).
Familiarity with HTML and CSS basics (helpful but not required).

Step 1: Setting Up Your Environment

Installing Libraries

First, let's install the necessary Python libraries. Open your terminal and run the following command:

pip install requests beautifulsoup4

These libraries will help us make HTTP requests (requests) to fetch web pages and parse HTML (beautifulsoup4) to extract data.

Step 2: Writing the Web Scraping Script

Fetching HTML Content

Now, let's create a Python script named scraper.py. Open your favorite code editor and start by importing the required libraries:

import requests
from bs4 import BeautifulSoup

Next, define the URL of the BBC News website we want to scrape:

url = 'https://www.bbc.com/news'

Function to Fetch HTML Content

We'll create a function fetch_html to fetch the HTML content from a given URL using requests:

def fetch_html(url):
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an HTTPError for bad responses
        return response.text
    except requests.exceptions.RequestException as e:
        print(f"Error fetching HTML: {e}")
        return None

This function sends a GET request to the URL and returns the HTML content if successful. It handles exceptions to ensure robust error handling.

Function to Scrape Website for News Headlines

Now, let's define a function scrape_website to parse the HTML and extract news headlines using BeautifulSoup:

def scrape_website(url):
    html = fetch_html(url)
    if html:
        soup = BeautifulSoup(html, 'html.parser')
        headlines = soup.find_all('h3', class_='gs-c-promo-heading__title')
        for headline in headlines:
            title = headline.text.strip()
            print(title)
    else:
        print("Failed to fetch HTML.")

Here's what this function does:

It calls fetch_html(url) to get the HTML content of the BBC News page.
If the HTML content is retrieved (if html:), it uses BeautifulSoup to parse the HTML (soup = BeautifulSoup(html, 'html.parser')).
It then finds all <h3> elements with the class gs-c-promo-heading__title, which typically contain news headlines on the BBC News website.
For each headline found (for headline in headlines:), it extracts the text (headline.text.strip()) and prints it.

Running the Script

To execute the scraping script, add the following code at the end of scraper.py:

if __name__ == "__main__":
    scrape_website(url)

This will run the scrape_website function when you run python scraper.py in your terminal.

Step 3: Handling Data and Output

Storing Data

To store the extracted headlines in a structured format (e.g., CSV or JSON), you can modify the scrape_website function to save the data into a file instead of printing it.

Advanced Scraping Techniques

For more advanced scraping tasks, you might explore:

Handling pagination (navigating through multiple pages of results).
Dealing with dynamic content (using tools like Selenium for JavaScript-heavy websites).
Implementing rate limiting to avoid overwhelming the target website's servers.

Conclusion

Congratulations! You've built a web scraping tool with Python to extract news headlines from the BBC News website. Web scraping opens up possibilities for automating data collection tasks. Always scrape responsibly and respect the website's terms of service.

GitHub Repository

I've uploaded the complete code to GitHub. You can view it here.

Creating a YouTube Video Downloader with Python: A Step-by-Step Guide

Pranjol Das — Tue, 11 Jun 2024 06:26:06 +0000

Creating a YouTube Video Downloader with Python: A Step-by-Step Guide

Introduction

Hello devs! I'm excited to share my first open-source project with you—a YouTube Video Downloader built using Python. This tutorial will walk you through the process of creating your own video downloader, from setting up the environment to the final implementation. Whether you're a beginner or looking for a fun project to improve your Python skills, this guide is for you!

Prerequisites

Before we start, make sure you have the following:

Python installed on your machine
Basic understanding of Python programming
Internet connection to download necessary libraries

Step 1: Setting Up the Environment

First, we need to install the pytube library, which will handle the YouTube video downloading process. Open your terminal and run:

pip install pytube

Step 2: Writing the Code

Create a new Python file, youtube_downloader.py, and open it in your favorite code editor. Let's start by importing the necessary libraries:

from pytube import YouTube
import os

Next, let's create a function to download the video:

def download_video(url, save_path):
    try:
        yt = YouTube(url)
        ys = yt.streams.get_highest_resolution()
        print(f"Downloading: {yt.title}")
        ys.download(save_path)
        print("Download completed!")
    except Exception as e:
        print(f"Error: {e}")

Now, let's add the main part of our script that will prompt the user for the video URL and the save path:

if __name__ == "__main__":
    url = input("Enter the YouTube video URL: ")
    save_path = input("Enter the path where the video will be saved: ")
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    download_video(url, save_path)

Step 3: Running the Script

To run the script, open your terminal, navigate to the directory where your youtube_downloader.py file is located, and execute:

python youtube_downloader.py

Follow the prompts, and your video will be downloaded to the specified location!

Conclusion

Congratulations! You've just created a YouTube Video Downloader using Python. This project is a great way to practice working with external libraries and handling user input. Feel free to expand on this project by adding more features, such as downloading playlists or choosing video quality.

I'd love to hear your feedback and see how you enhance this project. Happy coding!

GitHub Repository

I've also uploaded the complete code to GitHub. Check it out here.

DEV Community: Pranjol Das

Things Every Programmer Should *NOT* Do

1. Don’t Ignore Error Messages

The Classic: “Eh, it works on my machine.”

2. Don’t Write Spaghetti Code

When Your Code Looks Like This:

3. Don’t Skip the Planning Phase

“I’ll just figure it out as I go.”

4. Don’t Neglect Version Control

“I don’t need Git for this small project.”

5. Don’t Underestimate Testing

“It worked in the last version, so it should work now.”

6. Don’t Forget to Take Breaks

“I’ll just finish this feature, then I’ll rest.”

7. Don’t Be Afraid to Ask for Help

“I should know this. I don’t want to seem dumb.”

8. Don’t Stop Learning

“I’m done learning; I know everything I need.”

Get Geeky with Python: Build a System Monitor with Flair

Introduction

What Does It Do?

Key Libraries Used

Breaking Down the Code

1. System Information

2. CPU Information

3. Memory Information

4. GPU Information

5. Displaying the Information

Example Output

Wrapping Up

Building a Web Scraping Tool with Python: Extracting News Headlines

Introduction

Prerequisites

Step 1: Setting Up Your Environment

Installing Libraries

Step 2: Writing the Web Scraping Script

Fetching HTML Content

Function to Fetch HTML Content

Function to Scrape Website for News Headlines

Running the Script

Step 3: Handling Data and Output

Storing Data

Advanced Scraping Techniques

Conclusion

GitHub Repository

Creating a YouTube Video Downloader with Python: A Step-by-Step Guide

Creating a YouTube Video Downloader with Python: A Step-by-Step Guide

Introduction

Prerequisites

Step 1: Setting Up the Environment

Step 2: Writing the Code

Step 3: Running the Script

Conclusion

GitHub Repository

Things Every Programmer Should NOT Do